Background. Mycobacterium tuberculosis , the bacterium responsible for tuberculosis, remains a leading cause of global infectious disease deaths. While genomic epidemiology is essential for understanding transmission dynamics and the rise of antibiotic resistance, significant computational limitations currently prevent comprehensive phylogenetic analysis of the vast number of publicly available M. tuberculosis genomes. Methods. We developed UShER-TB, a comprehensive collection of community resources, including a computational analysis pipeline designed for scalable phylogenomic analysis of M. tuberculosis . To demonstrate this system’s utility, we processed 129,312 genomes to construct a global phylogeny. We evaluated the system’s accuracy in transmission cluster reconstruction, its ability to identify novel genetic lineages and its performance in placing ancient DNA samples within the global tree. Results. The pipeline successfully constructed a global phylogeny capturing genomic diversity from over 129,000 samples. UShER-TB achieved high accuracy in reconstructing transmission clusters, reaching an adjusted Rand index of 0.95 when compared to conventional phylogenomic methods. This tool also facilitated the identification of putative novel lineages and sublineages. Furthermore, the platform enabled the successful phylogenetic placement of ancient samples. The system allows for real-time analysis of new genomes to reveal transmission dynamics and introduction patterns on a global scale. Conclusions. This approach overcomes longstanding computational barriers in genomic surveillance. UShER-TB provides researchers with efficient tools for tuberculosis monitoring, particularly for resource-limited settings where the disease burden is highest. Trial registration. Not applicable.
Although mechanistic links between the microbiome and chronic disease are increasingly well defined, how microbiomes facilitate normal variation in host metabolism remains less clear. Building on our recent demonstration that pooled faecal inocula from different primate species can cause relative differences in mouse host adiposity, we define potential mechanisms by which this could occur. Using existing samples from germ-free mice inoculated with faecal samples from humans ( Homo sapiens ), rhesus macaques ( Macaca mulatta ) and squirrel monkeys ( Saimiri boliviensis ), we integrate shotgun metagenomics of caecal contents, liquid chromatography–MS-MS metabolomics of serum and caecal contents and hepatic RNA-sequencing. Our multi-omic analysis reveals that the relative increase in adiposity previously observed in macaque-donor mice is associated with differences in hepatic cysteine catabolism and bile metabolism pathway features. These data suggest that bile and sulphur co-metabolic variation may mediate increased bile pool hydrophobicity. We also observed variation in caecal microbiome composition as well as host hepatic retinol signalling and lipid metabolism between treatments. When considered together with the observation that macaque-donor mice consumed less food than the other treatment groups, our results support the hypothesis that increased bile pool hydrophobicity results in increased dietary energy extraction. Our findings provide a foundation for future mechanistic testing of new hypotheses for understanding how the intestinal microbiomes of different primate species regulate lipid homeostasis in a mouse host.
Lactococcus lactis and Lactococcus cremoris are widely exploited in global dairy fermentations. Consequently, strains of these species are extensively studied, as are their interactions with their infecting bacteriophages. In recent years, the number of lactococcal species has increased significantly and the ecology of these species extends far beyond the dairy niche. Furthermore, this expansion has also warranted the reassignment of several former lactococcal species to the genus Pseudolactococcus . In the present study, we explored the bacteriophage-relevant features of 495 genomes within the Lactococcus–Pseudolactococcus clade, including regions associated with (1) cell wall polysaccharides (CWPS) and exopolysaccharides (EPS) as possible bacteriophage receptors, (2) prophages and phage satellites and (3) anti-phage systems. This analysis revealed the universal presence and consistent genomic location of cwps loci, with extensive relationships between the loci of distinct species of both genera. It was also established that EPS-associated gene clusters are present in approximately half of the evaluated species and occur in a strain-dependent manner, suggesting that they are associated with niche adaptation rather than a core biological function of these organisms. Significant diversity and phylogenetic overlap of prophages across several species of both genera were observed. While prophages were widespread, phage satellites were especially prevalent among dairy-associated species. At the strain level, prophage, satellite and defence-system abundances were positively correlated. Restriction–modification systems appeared to form the core defence strategy of the Lactococcus–Pseudolactococcus taxon, complemented by species- and strain-level tailoring of the anti-phage repertoire to respond to phage pressure in diverse ecological niches. Niche-associated differences observed within L. lactis underscored that greater apparent phage exposure was not uniformly reflected across all phage-relevant features: dairy-associated strains carried more prophages, satellites and defence systems than plant-associated strains, whereas plant isolates exhibited greater rarefied polysaccharide pellicle richness. Overall, this represents the most comprehensive genome-based analysis of members of the Lactococcus–Pseudolactococcus clade, guiding our current understanding of the evolutionary pathways underlying phage–host co-adaptation followed by these micro-organisms.
Plasmids are mobile genetic elements with autonomous replication, whose ecology extends beyond individual bacterial hosts. As molecular symbionts, they have the potential to traverse bacterial taxa and environments, disseminating adaptive genes and shaping microbial community structure through dynamics that are likely decoupled from host taxonomy. Plasmidomics – the omics discipline dedicated to the study of plasmids – has revealed that plasmid diversity and distribution respond to environmental gradients independently of their hosts, underscoring their roles as ecological entities. However, the field faces critical methodological constraints: short-read assemblies fragment plasmid sequences, culture-dependent approaches underrepresent environmental diversity and most metagenomic methods fail to capture plasmid–host associations. In addition, a universally accepted classification framework is still lacking. Advancing plasmidomics will require the integration of long-read sequencing, Hi-C proximity ligation, meta-epigenomics and ecology-informed classification frameworks grounded in genomic and functional criteria. Together, these approaches are essential to uncover the true diversity, evolutionary significance and ecological dynamics of plasmids across complex microbial ecosystems.
The gut microbiome is an essential metabolic organ influencing host health through metabolite production. While metabolite production levels can be directly measured by gas or liquid chromatography, they are commonly inferred from the abundance of metagenomic functional pathways. To evaluate the accuracy of these inferences, we established a single, manually curated metabolite-pathway/enzyme mapping list as a standardized biological reference. We then compared liquid chromatography–mass spectrometry (LC–MS)-based faecal metabolites with functional pathways/enzymes inferred from four approaches: 16S rRNA gene amplicons, reference-based shotgun, de novo assembly-based contigs and de novo assembly-based metagenome-assembled genomes (MAGs). Our results demonstrate that predictive accuracy is strongly metabolite-specific and method-dependent rather than a uniform characteristic of metagenomic data. While 16S (9.9%), ref-shotgun (14.2%) and de novo -contigs (5.5%) yielded only a small fraction of well-predicted metabolites, the de novo -MAGs approach significantly outperformed other approaches, achieving a 36.9% well-predicted rate for mapped metabolites. Notably, this approach provided the most robust functional-metabolite associations for indicators of gut health. In conclusion, while microbial functional potential does not always mirror metabolic reality, high-quality genomic binning via MAGs offers a significantly more robust framework for selective metabolite prediction.
